Skip to content

Latest commit

 

History

History
100 lines (67 loc) · 3.77 KB

README.md

File metadata and controls

100 lines (67 loc) · 3.77 KB
Raw

Header

Summary

Project

This is an open source implementation of the Logical Analysis of Data (LAD) Algorithm.

Description

Logical Analysis of Data (LAD) is a rule-based machine learning algorithm based on ideas from Optimization and Boolean Function Theory. The LAD methodology was originally conceived by Peter L. Hammer, from Rutgers University, and has been described and developed in a number of papers since the late 80's. It has also been applied to classification problems arising in areas such as Medicine, Economics, and Bioinformatics. A list with representative publications about LAD will be made available here shortly.

LAD algorithm consists of dectecting hidden patterns capable of distinguishing observations in one class from all the other observations. The patterns are human readable which are used for reasoning of the decisions made by the classifier.

A Java implementation of a binary LAD classifier can be found here.

Related publications

  • Maximum Patterns in Datasets. Bonates, T.O., P.L. Hammer, A. Kogan. Discrete Applied Mathematics, vol. 156(6), 846-861, 2008. Link

  • An Implementation of Logical Analysis of Data. Boros, E., P.L. Hammer, T. Ibaraki, A. Kogan, E. Mayoraz, I. Muchnik. IEEE Transactions on Knowledge and Data Engineering, vol 12(2), 292-306, 2000. Link

  • Classificação Supervisionada de Dados via Otimização e Funções Booleanas. Gomes, V.S.D., T. O. Bonates. Anais do II Workshop Técnico-Científico de Computação, p.21-27, Mossoró, RN, Brazil, 2011.

Example

As the code was implemented following sklean's classifiers documentation, its usage is quite straightforward. See the code below.

from lad.lad import LADClassifier

from sklearn import datasets
from sklearn.model_selection import cross_val_score

# Dataset
X, y = datasets.load_iris(return_X_y=True)

# Classifier
lad = LADClassifier()

# CV
scores = cross_val_score(lad, X, y, cv=10, scoring="accuracy")

The current version of lad doesn't implement a score function!

Please, refer to the examples.py file for another example.

Installation

Choose one of the following in order to install this classifier.

  1. Clone this repository and use the setup file to install:

    $ git clone https://github.com/vauxgomes/lad.git
    $ sudo python setup.py install

  2. Install it with pip:

    $ python -m pip install git+https://github.com/vauxgomes/lad.git#egg=lad

Versions and tags

Tag Description Algorithms Status
v0.1 Uses pandas for processing the data and build decision rules. MaxPatterns Published
v0.2 Uses numpy instead of pandas. MaxPatterns Published
v0.3 The LAD lazy mode MaxPatterns, LazyMaxPatterns Published
v0.5 Using confidence and support to build lazy rules MaxPatterns, LazyPatterns Published
v1.0 Fully documented code MaxPatterns, LazyMaxPatterns --

Please refer to the release branch for the latest updates

Citation

In case you want to cite this project:

@software{V.S.D. Gomes,
    author = {Gomes, Vaux Sandino Diniz},
    month = {4},
    title = {{Logical Analysis of Data a Python Implementation}},
    version = {1.0.0},
    year = {2022}
}